BigQuery and Cloud Machine Learning

Kaz Sato is Staff Developer Advocate at Cloud Platform team, Google Inc. He leads the developer advocacy team for Machine Learning and Data Analytics products, such as TensorFlow, Cloud ML, and BigQuery. Speaking at major events including Google I/O 2016, Hadoop Summit 2016, Strata+Hadoop World 2016 San Jose and NYC, ODSC East/West 2016, Google Next 2015 NYC and Tel Aviv. Kaz also has been leading and supporting developer communities for Google Cloud for over 7 years. He is also interested in hardwares and IoT, and has been hosting FPGA meetups since 2013.

Presentation Description

Level: Beginner

Type: Technical

Length: 41:46

Tools used: TensorFlow, BigQuery

Overview:

The real value of BigQuery is not its speed. It's the power of "democratizing enterprise data." Because of BigQuery's scalability, you can isolate any workload on BigQuery from others. That means you can let non-engineers, such as sales, marketing, support and others, execute arbitrary quick-and-dirty SQL on BigQuery directly. Any employees in your enterprise can access its big data and quickly do data analytics without affecting performance to the production system.

Now, imagine what would happen if you could use BigQuery for deep learning as well. After having data scientists training the cutting edge intelligent neural network model with TensorFlow or Google Cloud Machine Learning, you can move the model to BigQuery and execute predictions with the model inside BigQuery. This means you can let any employee in your company use the power of BigQuery for their daily data analytics tasks, including image analytics and business data analytics on terabytes of data, processed in tens of seconds, solely on BigQuery without any engineering knowledge.

In this session, we'll look at how you can combine Cloud Machine Learning and BigQuery to realize this vision.

By sharing a demo, you'll see how BigQuery's power of "democratizing enterprise data" can be enhanced with a deep neural network model trained with Cloud Machine Learning.

Learning outcomes:

Learn how to combine BigQuery and Deep Learning problems.

Learn how to use several datasets available in BigQuery.

Learn specific applications of machine learning like forecasting, and document and image similarities with BigQuery.

Talk Timeline

0:00 - 7:34: Introduction and Overview

How to combine data warehouse technology and machine learning

What is BigQuery

Example of the performance of a full scan on 10B rows. In BigQuery you don’t need an index to get better performance.

7:34 - 20:29: BigQuery

Demo of document similarity from 10 million documents from StackOverflow.

20:29 - 37:11: Machine Learning Engine

Demo of TensorFlow. Description of the APIs and pre-trained machine learning models.

Demo of BigQuery for signature-based search. Using one million images with BigQuery.